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In a brief-but-amazing McKinsey interview with Chamath Palihapitiya, the venture capitalist
and former Facebook exec outlines three technologies that
most excite him: sensor networks (such as asthma inhalers that
can help avoid massive attacks and ER visits), autonomous
vehicles (“the one thing that I’ve seen that could fundamentally
have the high-order-bit2 effect
on GDP.

You can completely reenvision cities, transportation models, and
commerce with all these autonomous vehicles, with the ability to
ship goods”), and big data (genetics will shift from biologists
to computer scientists). That last point in particular reminds me
of Clayton Christensen’s forecasts of precision medicine.

Two other interesting bits. One, Palihapitiya sees
understanding technology as akin to learning a language, and
schools to need to facilitate this understanding:

So if we
thought it was really important for everyone in the United States
to speak English, and hopefully for a large majority maybe to
speak Spanish, why shouldn’t people understand how to “speak”
JavaScript? I don’t know. And how do you think about now
graduating or matriculating millions and millions of kids who
“speak” technology as proficiently as they speak a verbal
language?

And probably what you find is, if you actually had knowledge of a
technical language, you would probably “speak” that language more
in your daily life than the actual verbal language. I think
coding is the blue-collar job of the 21st century. There’s
nothing wrong with that. We are in a world right now where these
abstractions are getting so good. What it meant to code 10 or 15
years ago when I was learning was actually a very difficult
premise, in my opinion.

These are extremely low-level languages. You’re dealing with
hardware in a way that you don’t have to, today. We’re so well
abstracted that, in four or five years, my children will code by
drawing things on a page and it will translate it into code. So
what it means “to code” is becoming a simpler definition, which
means by extension that more people should be able to do it.

Palihapitiya also gives a pretty potent description of the nexus
of technology, and education, and inequality:

There’s an arc
of technical proficiency that’s lacking in most companies.
There’s an arc of rewards and recognition that tends to lag and
tends to not feed the top 1 percent or 5 percent but tends to
manage to the middle. Those are extremely inherent biases that
have existed in companies for decades.

But when you see the few companies that get it right, what
they’ve done is they’ve disrupted those three specific things.
They’ll say, “OK, you know what? It’s all about the top 1
percent. Everyone else, tough luck. We celebrate the best, and
everybody else can tag along. We cull the bottom, and we’re super
aggressive. We have an extremely deep quantitative understanding
of our business.